Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings

The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P < .001) and isolated tumor cells (67.95% to 96.15%, P < .001) in a significantly shorter review time (−31.5%, P < .001). Cross-site evaluation indicates that the algorithm is highly robust (AUC = 0.9829).

this limitation, our proposed method (Fig. 7b) leverages a property of affine transformation where a patch of a transformed image is only associated with an effective region of limited size from the original image. Transforming a small region instead of an entire WSI substantially reduces the memory footprint and thus prevents thrashing. We present detailed procedures for the calculation of the effective region, followed by the remaining steps necessary for obtaining an augmented patch.
Let I: R 2 → [0, 1] 3 denote a WSI, defining the RGB output I(v) ∈ [0, 1] 3 given an input coordinate v ∈ R 2 . Although WSIs as raster graphics store RGB values on grid points, they can be extended to R 2 through interpolation (bilinear interpolation in our implementation) and white-padding. Herein, we define coordinates to be zero centered; that is, the coordinate of the image center is (0, 0). The underlying spatial mapping of coordinates of an affine transformation is defined as f(v) = Av + b, where an invertible matrix A ∈ R 2 × 2 and b ∈ R 2 are the parameters of f(.). We denote the transformed image as I′: R 2 → [0, 1] 3 such that ∀v′, I′(v′) = I(f −1 (v′)).
When a request to access a patch on the transformed image is received, the first step is locating the center of the effective region in the original image. When the transformed patch center is v 0 ′, the center of the effective region can be obtained by f −1 (v 0 ′), denoted as v 0 . The second step entails calculating the span of the effective region. For the width and height of the requested patch as w′ and h′, the algorithm calculates both the width and height of the squared effective region by using a = √(w′ 2 + h′ 2 ) / ||A|| 2 . In the third step, the effective region is cropped out at v 0 with both width and height as a. The cropped effective region represented by P: R 2 → [0, 1] 3 conforms to the equations ∀v ∈ [−a / 2, a / 2] 2 , P(v) = I(v + v 0 ). The fourth step involves the application of f′(v) = Av, an affine transformation without translation, to the cropped region, thus retrieving a transformed region denoted as P′: R 2 → [0, 1] 3 , where ∀v′ ∈ [−a / 2, a / 2] 2 , P′(v′) = P(f′ −1 (v′)). Finally, the desired patch with the width w and height h is obtained by centrally cropping the transformed region P′(.). Although this method for retrieving a transformed patch is more complex than simply cropping the patch from a transformed WSI, their outcomes are equivalent because ∀v′ ∈ ([−w / 2, w / 2], [−h / 2, h / 2]), P′(v′) = P(f′ −1 (v′)) = P(A −1 v′) = I(A −1 v′ + v 0 ) = I(A −1 v′ + f −1 (v 0 ′)) = I(A −1 v′ + A −1 (v 0 ′ -b)) = I(A −1 (v′ + v 0 ′ -b)) = I(f −1 (v′ + v 0 ′)) = I′(v′ + v 0 ′). Furthermore, the present method circumvents the thrashing that is characteristic of affine transformations on WSIs.

Supplementary Tables
Supplementary Table 1 | Extended performance table of our  Considering the between-study discrepancies in test slide distributions, the results may contain bias.

Discussion
Implications 20 D;V Discuss the potential clinical use of the model and implications for future research.

Discussion
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